VeTra：一种基于RNA速度的轨迹推断工具。
VeTra: a tool for trajectory inference based on RNA velocity.
作者信息
Weng Guangzheng, Kim Junil, Won Kyoung Jae
机构信息
Department of Biology, The Bioinformatics Centre, University of Copenhagen, 2200 Copenhagen N, Denmark.
Biotech Research and Innovation Centre (BRIC), University of Copenhagen, 2200 Copenhagen N, Denmark.
出版信息
Bioinformatics. 2021 Oct 25;37(20):3509-3513. doi: 10.1093/bioinformatics/btab364.
MOTIVATION
Trajectory inference (TI) for single cell RNA sequencing (scRNAseq) data is a powerful approach to interpret dynamic cellular processes such as cell cycle and development. Still, however, accurate inference of trajectory is challenging. Recent development of RNA velocity provides an approach to visualize cell state transition without relying on prior knowledge.
RESULTS
To perform TI and group cells based on RNA velocity we developed VeTra. By applying cosine similarity and merging weakly connected components, VeTra identifies cell groups from the direction of cell transition. Besides, VeTra suggests key regulators from the inferred trajectory. VeTra is a useful tool for TI and subsequent analysis.
AVAILABILITY AND IMPLEMENTATION
The Vetra is available at https://github.com/wgzgithub/VeTra.
SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
动机
单细胞RNA测序（scRNAseq）数据的轨迹推断（TI）是解释细胞周期和发育等动态细胞过程的有力方法。然而，准确推断轨迹仍然具有挑战性。RNA速度的最新发展提供了一种在不依赖先验知识的情况下可视化细胞状态转变的方法。
结果
为了基于RNA速度进行TI并对细胞进行分组，我们开发了VeTra。通过应用余弦相似度和合并弱连接组件，VeTra从细胞转变方向识别细胞组。此外，VeTra从推断的轨迹中提出关键调节因子。VeTra是TI及后续分析的有用工具。
可用性和实现方式
VeTra可在https://github.com/wgzgithub/VeTra获得。
补充信息
补充数据可在《生物信息学》在线获取。
Zotero 插件